Python Programming with Machine Learning by Saqib Gulzar Bhat

Module 1: Introduction to Programming & Python Basics

• What is Programming? Applications of Python

• Installing Python, Anaconda, Jupyter Notebook

• Python Syntax, Indentation, Comments

• Variables and Data Types

• Input / Output Operations

• Type Casting

Outcome: Learners understand core Python syntax and environment.

Module 2: Control Flow & Data Structures

• Conditional Statements (if, if-else, elif)

• Looping (for, while, nested loops)

• Break, Continue, Pass

• Python Data Structures:

  • Lists

  • Tuples

  • Sets

  • Dictionaries

• Common Built-in Functions

Outcome: Ability to write logic-driven programs.

Module 3: Functions & Modular Programming

• Defining and Calling Functions

• Parameters and Return Values

• Default and Keyword Arguments

• Lambda Functions

• Recursion

• Modules and Packages

• Python Standard Library Overview

Outcome: Writing reusable and modular code.

Module 4: Strings, Files & Exception Handling

• String Operations and Methods

• File Handling (read, write, append)

• Working with CSV and Text Files

• Exception Handling (try, except, finally)

• Custom Exceptions

• Debugging Techniques

Outcome: Data handling and error-free programming.

Module 5: Object-Oriented Programming (OOP) in Python

• OOP Concepts: Class, Object

• Constructors and Destructors

• Inheritance

• Polymorphism

• Encapsulation and Abstraction

• Method Overriding

• Real-world OOP Examples

Outcome: Understanding real-world software design.

Module 6: Advanced Python Concepts

• Iterators and Generators

• Decorators

• List, Dictionary & Set Comprehensions

• Regular Expressions

• Date and Time Handling

• Virtual Environments

• Performance Optimization Basics

Outcome: Writing efficient and advanced Python programs.

Module 7: Working with Libraries & Data Handling

• Introduction to NumPy

  • Arrays, Operations, Broadcasting

• Introduction to Pandas

  • Series and DataFrames

  • Data Cleaning and Manipulation

• Data Visualization Basics

  • Matplotlib

  • Seaborn (optional)

Outcome: Data handling skills required for ML.

Module 8: Introduction to Statistics & Linear Algebra (ML Prerequisites)

• Types of Data

• Mean, Median, Mode

• Variance and Standard Deviation

• Correlation and Covariance

• Basics of Linear Algebra:

  • Vectors and Matrices

• Probability Fundamentals

Outcome: Mathematical intuition for Machine Learning.

Module 9: Introduction to Machine Learning

• What is Machine Learning?

• Types of ML:

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

• ML Workflow

• Real-world ML Applications

Outcome: Conceptual understanding of ML.

Module 10: Machine Learning with Python

• Introduction to Scikit-Learn

• Data Preprocessing

  • Handling Missing Values

  • Feature Scaling

• Train-Test Split

• Model Evaluation Metrics

Module 11: Basic Machine Learning Algorithms

• Linear Regression

• Logistic Regression

• k-Nearest Neighbors (KNN)

• Decision Trees

• K-Means Clustering

Outcome: Ability to build simple ML models.

Module 12: Mini Projects & Capstone

• Python Mini Projects:

  • Student Management System

  • File-Based Applications

• ML Mini Projects:

  • House Price Prediction

  • Spam Email Classification

• Final Capstone Project (End-to-End)

Outcome: Practical implementation & confidence building